Two PhD studentships available on assistive and rehabilitation technology

We have two PhD Studentships available at the University of Hertfordshire, School of Computer Science:

A- Fall detection and activity monitoring at home

With the growing ageing population and preference for prolonged personal independence, smart home technology and telecare has gained substantial popularity. The PhD focuses on detecting user status at home or residential care settings using commercially off the shelf technology. It uses machine learning algorithms to classify and detect resident activities and events of interests including falls. The research is linked with local residential care enterprises and North Hertfordshire County Council, and benefits from cross disciplinary supervision between the School of Computer Science and the Centre for Research in Primary and Community Care at the University. Technologies available include fish-eye ceiling cameras, contact sensors, Kinect and RGB-D cameras, wireless range and position sensors, to name a few.

B- Sensing user input in HRI using information theory

Perceiving sensory information better allows us to provide more personalised human-robot interaction. An area of interest is to provide a mechanism where human and robot inputs can be separated towards personalisation and better control. The focus of this PhD is to utilise information theory to better sense user inputs during interaction and to provide a better sense of being in control, or empowerment. This is then used to alter conventional control paradigms such as impedance and admittance control, to provide a more personalised control. HapticMASTER robot as well as EMG and EEG systems are available for use within this PhD. The outcome has application in general HRI but specifically useful for unsupervised therapeutic interaction with robots after stroke. The goal is to use information theory for information provision to patients and their therapist, to inform on recovery progress, and better direct choice of therapeutic interactions.

Requirements: Applicants should have a very strong first degree or (preferably) a Master’s degree in Cybernetics, Computer Science, Biomechanics or other relevant area, and are expected to have strong interdisciplinary interests (e.g. in robotics, rehabilitation, neuroscience). They are also expected to have very good programming skills and interest in robotics. The PhD will be conducted under Dr Farshid Amirabdollahian’s supervision and candidates are invited to informally contact f.amirabdollahian2 (at) herts.ac.uk.

A limited number of studentships are available for exceptional candidates in particular areas (approximately £14,250 per annum bursary plus the payment of the student fees). Applicants from outside the UK or EU are eligible.

Research in Computer Science at the University of Hertfordshire has been recognised as excellent in the REF 2014, with 50% of the research submitted rated as internationally excellent or world leading.
The Science and Technology Research Institute provides a very stimulating environment, offering a large number of specialised and interdisciplinary seminars as well as general training and researcher development opportunities. The University of Hertfordshire is situated in Hatfield, in the green belt just north of London.

Your application form​ should be returned to:
Mrs Lorraine Nicholls
Research Student Administrator,
STRI
University of Hertfordshire,
College Lane
Hatfield, Herts,
AL10 9AB
tel: +44 (0)1707 286083
l.nicholls (at) herts.ac.uk

Applications should also include two references and transcripts of previous academic degrees. We accept applications for self-funded places throughout the year.The next short-listing process for studentship applications will begin on
30 May 2016.

New role of “Theme Champion in Information and Security”

I have been appointed to the role of theme champion in Information and Security.
The idea behind this role is to provide an interface connecting the multidisciplinary work in research areas such as robotics, biocomputation, algorithms, artificial intelligence, cybersecurity, networking, materials, etc, with the opportunities for funding, spin-offs and partnerships outside the University. More on this role to follow after the launch event scheduled for May 12th.

New article on JNER describing results from SCRIPT project

Our latest article describing the results from SCRIPT project is now online on JNER (open access, here)

Here is a copy of the abstract:

Background

Assistive and robotic training devices are increasingly used for rehabilitation of the hemiparetic arm after stroke, although applications for the wrist and hand are trailing behind. Furthermore, applying a training device in domestic settings may enable an increased training dose of functional arm and hand training. The objective of this study was to assess the feasibility and potential clinical changes associated with a technology-supported arm and hand training system at home for patients with chronic stroke.

Methods

A dynamic wrist and hand orthosis was combined with a remotely monitored user interface with motivational gaming environment for self-administered training at home. Twenty-four chronic stroke patients with impaired arm/hand function were recruited to use the training system at home for six weeks. Evaluation of feasibility involved training duration, usability and motivation. Clinical outcomes on arm/hand function, activity and participation were assessed before and after six weeks of training and at two-month follow-up.

Results

Mean System Usability Scale score was 69 % (SD 17 %), mean Intrinsic Motivation Inventory score was 5.2 (SD 0.9) points, and mean training duration per week was 105 (SD 66) minutes. Median Fugl-Meyer score improved from 37 (IQR 30) pre-training to 41 (IQR 32) post-training and was sustained at two-month follow-up (40 (IQR 32)). The Stroke Impact Scale improved from 56.3 (SD 13.2) pre-training to 60.0 (SD 13.9) post-training, with a trend at follow-up (59.8 (SD 15.2)). No significant improvements were found on the Action Research Arm Test and Motor Activity Log.

Conclusions

Remotely monitored post-stroke training at home applying gaming exercises while physically supporting the wrist and hand showed to be feasible: participants were able and motivated to use the training system independently at home. Usability shows potential, although several usability issues need further attention. Upper extremity function and quality of life improved after training, although dexterity did not. These findings indicate that home-based arm and hand training with physical support from a dynamic orthosis is a feasible tool to enable self-administered practice at home. Such an approach enables practice without dependence on therapist availability, allowing an increase in training dose with respect to treatment in supervised settings.

Trial registration

This study has been registered at the Netherlands Trial Registry (NTR): NTR3669.

New publication from SCRIPT project

The experience of living with stroke and using technology: opportunities to engage and co-design with end users

Purpose: We drew on an interdisciplinary research design to examine stroke survivors’ experiences of living with stroke and with technology in order to provide technology developers with insight into values, thoughts and feelings of the potential users of a to-be-designed robotic technology for home-based rehabilitation of the hand and wrist. Method: Ten stroke survivors and their family carers were purposefully selected. On the first home visit, they were introduced to cultural probe. On the second visit, the content of the probe packs were used as prompt to conduct one-to-one interviews with them. The data generated was analysed using thematic analysis. A third home visit was conducted to evaluate the early prototype. Results: User requirements were categorised into their network of relationships, their attitude towards technology, their skills, their goals and motivations. The user requirements were used to envision the requirements of the system including providing feedback on performance, motivational aspects and usability of the system. Participants’ views on the system requirements were obtained during a participatory evaluation. Conclusion: This study showed that prior to the development of technology, it is important to engage with potential users to identify user requirements and subsequently envision system requirements based on users’ views.Implications for Rehabilitation

  • An understanding of how stroke survivors make sense of their experiences of living with stroke is needed to design home-based rehabilitation technologies.

  • Linking stroke survivors’ goals, motivations, behaviour, feelings and attitude to user requirements prior to technology development has a significant impact on improving the design.

Read More: http://informahealthcare.com/doi/abs/10.3109/17483107.2015.1036469

New PhD students in Assistive and Rehabilitation Robotics Team

The following new team members have joined us recently:

  • Miss Udeshika Dissanayake, researching in the area of rehabilitation robots and QEEG feedback
  • Miss Bernadette Iyawe, researching in the area of designing assistive and haptic affordances for the blind and partially sighted
  • Mr Sudhir Sharma, researching serious games and robotics for rehabilitation
  • Mr Azeemsha Thacham Poyil, researching combining robotics with EMG and QEEG